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Home   /   Post Doctorat   /   Researcher in Artificial Intelligence applied to self-driven microfluidic

Researcher in Artificial Intelligence applied to self-driven microfluidic

Computer science and software Engineering sciences


This postdoctoral position is part of the 2FAST project (Federation of Fluidic Autonomous labs to Speed-up material Tailoring), which is a part of the PEPR DIADEM initiative. The project aims to fully automate the synthesis and online characterization of materials using microfluidic chips. These chips provide precise control and leverage digital advancements to enhance materials chemistry outcomes. However, characterising nano/micro-materials at this scale remains challenging due to its cost and complexity. The 2FAST project aims to utilise recent advances in the automation and instrumentation of microfluidic platforms to develop interoperable and automatically controlled microfluidic chips that enable the controlled synthesis of nanomaterials. The aim of this project is to create a proof of concept for a microfluidic/millifluidic reactor platform that can produce noble metal nanoparticles continuously and at high throughput. To achieve this, feedback loops will be managed by artificial intelligence tools, which will monitor the reaction progress using online-acquired information from spectrometric techniques such as UV-Vis, SAXS, and Raman. The postdoctoral position proposed focuses on AI-related work associated with the development of feedback loop design, creation of a signal database tailored for machine learning, and implementation of machine learning methods to connect various data and/or control autonomous microfluidic devices.


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